Outputs (21)

Genome-Wide Identification of the ABC Gene Family and Its Expression in Response to the Wood Degradation of Poplar in Trametes gibbosa (2024)
Journal Article
Zhao, J., Wang, A., & Wang, Q. (2024). Genome-Wide Identification of the ABC Gene Family and Its Expression in Response to the Wood Degradation of Poplar in Trametes gibbosa. Journal of Fungi, 10(2), Article 96. https://doi.org/10.3390/jof10020096

Wood-rotting fungi’s degradation of wood not only facilitates the eco-friendly treatment of organic materials, decreasing environmental pollution, but also supplies crucial components for producing biomass energy, thereby reducing dependence on fossi... Read More about Genome-Wide Identification of the ABC Gene Family and Its Expression in Response to the Wood Degradation of Poplar in Trametes gibbosa.

DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring (2023)
Journal Article
Dai, S., Meng, F., Wang, Q., & Chen, X. (2024). DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring. Renewable and Sustainable Energy Reviews, 191, Article 114091. https://doi.org/10.1016/j.rser.2023.114091

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household level into appliance-level consumption, can help analyze the electricity consumption beha... Read More about DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring.

On Fine-tuned Deep Features for Unsupervised Domain Adaptation (2023)
Presentation / Conference Contribution
Wang, Q., Meng, F., & Breckon, T. (2023, June). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. Presented at IJCNN 2023: International Joint Conference on Neural Networks, Queensland, Australia

Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptati... Read More about On Fine-tuned Deep Features for Unsupervised Domain Adaptation.

Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders (2023)
Journal Article
Wang, Q., & Breckon, T. (2023). Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders. Neural Networks, 163, 40-52. https://doi.org/10.1016/j.neunet.2023.03.033

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classific... Read More about Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders.

Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation (2023)
Journal Article
Wang, Q., Meng, F., & Breckon, T. (2023). Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation. Neural Networks, 161, 614-625. https://doi.org/10.1016/j.neunet.2023.02.006

We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distributions or learn domain-invariant features, we directly learn a unified c... Read More about Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation.

Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation (2021)
Journal Article
Wang, Q., & Breckon, T. (2022). Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation. Pattern Recognition, 123, Article 108362. https://doi.org/10.1016/j.patcog.2021.108362

Heterogeneous Domain Adaptation (HDA) addresses the transfer learning problems where data from the source and target domains are of different modalities (e.g., texts and images) or feature dimensions (e.g., features extracted with different methods).... Read More about Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation.

Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties (2021)
Journal Article
You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022). Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties. Applied Energy, 305, Article 117899. https://doi.org/10.1016/j.apenergy.2021.117899

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT’s predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency and cost saving.energy efficiency,... Read More about Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties.

Source Class Selection with Label Propagation for Partial Domain Adaptation (2021)
Presentation / Conference Contribution
Wang, Q., & Breckon, T. (2021, September). Source Class Selection with Label Propagation for Partial Domain Adaptation. Presented at International Conference on Image Processing, Anchorage, AK

In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes a... Read More about Source Class Selection with Label Propagation for Partial Domain Adaptation.

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2021)
Presentation / Conference Contribution
Wang, Q., Bhowmik, N., & Breckon, T. (2020, December). Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020), Miami, Florida

Automatic detection of prohibited objects within passenger baggage is important for aviation security. X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibite... Read More about Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery.

A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks (2019)
Presentation / Conference Contribution
Wang, Q., Ning, J., & Breckon, T. (2019, September). A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks. Presented at 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan

Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the b... Read More about A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks.